# 统计代写|假设检验代写hypothesis testing代考|MA121

## 统计代写|假设检验代写hypothesis testing代考|It’s All About the Null Hypothesis

P-values are directly connected to the null hypothesis, as you hopefully remember from our discussion about sampling distributions for test statistics. So, we need to cover that first!

In all hypothesis tests, the researchers are testing an effect or relationship of some sort. The effect can be the effectiveness of a new vaccination, the durability of a new product, and so on. There is some benefit or difference that the researchers hope to identify.

However, it’s possible that there actually is no effect or no difference between the experimental groups. In statistics, we call this lack of an effect the null hypothesis. When you assess the results of a hypothesis test, you can think of the null hypothesis as the devil’s advocate position, or the position you take for the sake of argument.

To understand this idea, imagine a hypothetical study for medication that we know is entirely useless. In other words, the null hypothesis is true. There is no difference in patient outcomes at the population level between subjects who take the medication and subjects who don’t.

Despite the null being accurate, you will likely observe an effect in the sample data due to random sampling error. It is improbable that samples will ever exactly equal the null hypothesis value.

Think back to those t-distributions centered on zero for no effect. With those distributions, we noticed that it is not unusual to have a sample effect even when the null hypothesis is correct.

## 统计代写|假设检验代写hypothesis testing代考|Defining P-values

P-values indicate the believability of the devil’s advocate case that the null hypothesis is correct given the sample data. They gauge how consistent your sample statistics are with the null hypothesis. Specifically, if the null hypothesis is right, what is the probability of obtaining an effect at least as large as the one in your sample?

• High p-values: Your sample results are consistent with a true null hypothesis.
• Low p-values: Your sample results are not consistent with a true null hypothesis.

If your p-value is small enough, you can conclude that your sample is so incompatible with the null hypothesis that you can reject the null for the entire population. P-values are an integral part of inferential statistics because they help you use your sample to draw conclusions about a population.
Here is the technical definition of p-values:
P-values are the probability of observing a sample statistic that is at least as extreme as your sample statistic when you assume that the null hypothesis is correct.

Let’s go back to our hypothetical medication study. Suppose the hypothesis test generates a $\mathrm{p}$-value of $0.03$. You’d interpret this $\mathrm{p}$-value as follows:

If the medicine has no effect in the population, $3 \%$ of studies will obtain the effect observed in your sample, or larger, because of random sample error.

# 假设检验代考

## 统计代写|假设检验代写hypothesis testing代考|It’s All About the Null Hypothesis

P 值与零假设直接相关，希望您从我们关于检验统计的抽样分布的讨论中还记得这一点。所以，我们需要先涵盖它！

## 统计代写|假设检验代写hypothesis testing代考|Defining P-values

P 值表示魔鬼拥护者案例的可信度，即给定样本数据原假设是正确的。他们衡量您的样本统计数据与零假设的一致性。具体来说，如果零假设是正确的，那么获得至少与样本中的效果一样大的效果的概率是多少？

• 高 p 值：您的样本结果与真实的原假设一致。
• 低 p 值：您的样本结果与真实的零假设不一致。

P 值是在假设原假设正确时观察到至少与样本统计量一样极端的样本统计量的概率。

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